Diagnosis is defined as the reasoning process making it possible to search for the failure or failures (also referred to as root causes) that may explain the observation of a set of effects. As in any reasoning process, diagnosis implements a calculation, the reasoning proper, and a knowledge set, often expressed in the form of relations between the failures and the effects observed.
This knowledge set is grouped together in a knowledge base. As shown in FIG. 1b, the diagnosis is carried out by a calculator also referred to as algorithmic core 20, with the help of the knowledge base 10 and of messages 51 bearing the information about the effects 50 observed on the system 200 by virtue of detection devices (sensors, testers, comparators, etc.). The information borne by the messages 51 is “the effect is present” or “the effect is absent”, as well as the address of the corresponding effect in the knowledge base.
The set consisting of knowledge base 10 and algorithmic core 20 is referred to as a diagnoser 100.
FIG. 1a illustrates a knowledge base generation method, known to the person skilled in the art.
The information constituting the knowledge base is initialized by the designer of the system under diagnosis 200 with the aid of a specific language suited to the field of maintenance. This language makes it possible to describe the relations between the failures 60 and the observed effects 50 as well as the messages 51 transmitted to the diagnoser of the system under diagnosis.
The current complexity of systems under diagnosis no longer allows system designers to establish intellectually the relations between the failures of their system and the observed effects. A model editor 31 allows the designer of the system under diagnosis to express with the aid of the field-specific description language, the behaviour of the components of the system under diagnosis 200, the relations between their failures 60, the observed effects 50 and the messages 51. The set of relations between an effect and its cause(s) (or failure(s)) is often referred to as the fault tree 33 of this effect; the relations between a failure and its effect(s) is called the “signature” of this failure.
These relations are illustrated by the example given with FIG. 2 which shows a set of failures C1, C2, C3 referenced 60 and their observed effects E1, E2, E3, referenced 50 as well as a failure C4 which has no observed effect. In principle, it is not possible to have an observed effect without a failure.
With the help of these relations between failures 60 and effects 50 it is possible to define the following signatures:                the signature of C1 Sig(C1) is {E1, E2, notE3},        the signature of C2 Sig(C2) is {E1, E2, notE3}, and        the signature of C3 Sig(C3) is {E1, E2, E3}.        
The fact that the effect Ex does not originate from a failure is indicated by “notEx”. For example here, the signature of C1 indicates that C1 produces the effect E1 and the effect E2 but does not produce the effect E3.
There are thus two failures C1 and C2 which have the same signature and which are therefore not distinguishable.
With the help of these logical relations between the failures and the effects, fault trees AF are also defined.
In this example presented here:                AF (E1)=C1 or C2 or C3        AF (E2)=C1 or C2 or C3        AF (E3)=C3        
The set of these relations, that is to say of these fault trees and signatures, constitutes the knowledge base. It may be noted that the fault trees can be viewed as the description of a knowledge base made with the help of the effects while the signatures can be seen as the description of a knowledge base made with the help of the causes.
The knowledge base 10 is generated by means of a knowledge base capture and generation device 30. The knowledge base 10, used by the diagnoser 100, contains the fault trees 33 produced by a fault tree 33 generator 32.
The operation of the algorithmic core 20 associated with the knowledge base 10 is now recalled in relation to FIG. 1b. 
When the diagnoser is undertaking the diagnosis of a system 200, the failures of the system under diagnosis 60 produce the observed effects 50; these observed effects are transformed into messages 51 which are dispatched directly to the algorithmic core 20, which with the aid of the knowledge base 10 establishes a diagnosis.
The search for a diagnosis amounts to searching through the set of fault trees associated with the set of observed effects 50 for the values “True” or “False” of the failures 60 which explain the value “True” of the set of observed effects 50 which is contained in the messages 51.
In the example of FIG. 2, if the observed effects are E2 and E3, the messages 51 “E2 is present” and “E3 is present” are dispatched to the algorithmic core which extracts the respective fault trees of E2 and E3 from the knowledge base, and then calculates the associated expression “AF(E2) and AF(E3) ” which equals: (C1 or C2 or C3) and C3.
The explanation for the presence of E2 and E3 is therefore as follows:                C3 is true,        or C1 and C3 are true,        or C2 and C3 are true.        
In mathematical language this operation is called a “logical satisfiability search”. In the field of diagnosis, the result of this search can be expressed in the following manner. In view of the observed effects E2 and E3, the explanation is as follows:                The failure C3 is present,        or the failures C1 and C3 are both present,        or the failures C2 and C3 are both present.        
Within the framework of this invention, one is endeavouring to measure the performance of the diagnoser 100. The system 200 is not undergoing diagnosis during the measurement of the performance of the diagnoser 100.
According to the prior art, the performance of a diagnoser is usually defined by means of a failure detection rate and location rate (for simplicity, the term “location rate” will be used).
The calculation of these rates is for example described in the testability calculation standards MIL-STD 2165 (Appendix A paragraph 50.7.3) or IEEE Std 1522.
The detection rate is calculated as the ratio of the number of failures 60 producing an observed effect 50 to the total number of potential failures contained by the system under diagnosis 200.
The location rate is related to the number of failures which produce identical observed effects and which therefore are not distinguishable.
The sets of failures which are not distinguishable are called an “ambiguity group”. In our example, C1 and C2 which have the same observed effects, that is to say the same signature, form an ambiguity group.
The location rate for 1, 2, 3, . . . N failures is customarily defined.
The location rates are calculated with the ratio of the number of failures of an ambiguity group to the number of detected failures. The location rate for 1 failure is defined as the ratio of the number of failures distinguishable in a unique manner to the total number of detectable failures; the location rate for 2 failures is defined as the ratio of the number of failures distinguishable pairwise to the total number of detectable failures; and so on and so forth.
These calculations are illustrated in conjunction with the example of FIG. 2.
C4 does not produce any effect. C1, C2, C3 are detectable, C4 not. The detection rate, such as defined, is obtained by calculating the number of observable failures, divided by the total number of failures, i.e. ¾: the detection rate is 75%.
C1 and C2 are not distinguishable since they have the same signature. Their ambiguity is therefore 2.
The signature of C3 is unique, the ambiguity of C3 is therefore 1.
The location rates such as defined will be calculated in the following manner:                the location rate of a single failure is ⅓ i.e. 33%,        the location rate of failures pairwise is ⅔ i.e. 66%.        
The manner of calculation described hereinabove in accordance with the prior art relies on the signatures extracted from the knowledge base indicating which are the values “effects produced” or “effect not produced” associated with the failure. This signifies specifically that the calculation scheme according to the prior art evaluates the performance allowed by the knowledge base but does not reflect the actual performance of the diagnoser which bases its diagnosis on the information actually transmitted by the messages 51 received by it.
In addition to this weakness, the rates are usually defined in the prior art in a global manner for the diagnoser as a whole. The ambiguity information is synthesized in the calculations of the rates. In this manner, the improvement in the performance of the diagnoser can only be processed globally without making it possible to target exactly where the effort to improve the performance should be produced.